NvidiaWarp-GarmentCode/warp/native/cutlass/tools/profiler/src/device_allocation.h

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/* \file
   \brief Execution environment
*/

#pragma once

#include <stdexcept>
#include <list>
#include <vector>

#include "cutlass/library/library.h"
#include "cutlass/util/distribution.h"

#include "enumerated_types.h"

/////////////////////////////////////////////////////////////////////////////////////////////////

namespace cutlass {
namespace profiler {

/////////////////////////////////////////////////////////////////////////////////////////////////

/// Device memory allocation
class DeviceAllocation {
private:

  /// Data type of contained elements
  library::NumericTypeID type_;

  /// Gets the stride between elements
  size_t batch_stride_;

  /// Capacity in elements of device allocation
  size_t capacity_;

  /// Pointer to device memory
  void *pointer_;

  /// Layout type ID
  library::LayoutTypeID layout_;

  /// Stride vector
  std::vector<int64_t> stride_;

  /// Extent vector
  std::vector<int> extent_;

  /// Support allocating a 'batch' of non-overlapping tensors in contiguous memory
  int batch_count_;

  /// Buffer holding TensorRef instance to recently allocated memory
  std::vector<uint8_t> tensor_ref_buffer_;

public:
  //
  // Static member functions
  //

  /// Determines the number of bytes needed to represent this numeric type
  static size_t bytes(library::NumericTypeID type, size_t capacity);

  /// Returns the stride of a packed layout
  static std::vector<int64_t> get_packed_layout(
    library::LayoutTypeID layout_id, 
    std::vector<int> const &extent);

  /// returns the capacity needed
  static size_t construct_layout(
    void *bytes,
    library::LayoutTypeID layout_id,
    std::vector<int> const &extent,
    std::vector<int64_t> &stride);

  /// Returns true if two blocks have exactly the same value
  static bool block_compare_equal(
    library::NumericTypeID numeric_type, 
    void const *ptr_A, 
    void const *ptr_B, 
    size_t capacity);

  /// Returns true if two blocks have approximately the same value
  static bool block_compare_relatively_equal(
    library::NumericTypeID numeric_type, 
    void const *ptr_A, 
    void const *ptr_B, 
    size_t capacity,
    double epsilon,
    double nonzero_floor);

public:
  //
  // Methods
  //

  DeviceAllocation();
  
  DeviceAllocation(library::NumericTypeID type, size_t capacity);
  
  DeviceAllocation(
    library::NumericTypeID type, 
    library::LayoutTypeID layout_id, 
    std::vector<int> const &extent, 
    std::vector<int64_t> const &stride = std::vector<int64_t>(),
    int batch_count = 1);

  ~DeviceAllocation();

  DeviceAllocation &reset();

  /// Allocates device memory of a given type and capacity
  DeviceAllocation &reset(library::NumericTypeID type, size_t capacity);

  /// Allocates memory for a given layout and tensor
  DeviceAllocation &reset(
    library::NumericTypeID type, 
    library::LayoutTypeID layout_id, 
    std::vector<int> const &extent, 
    std::vector<int64_t> const &stride = std::vector<int64_t>(),
    int batch_count = 1);

  /// Returns a buffer owning the tensor reference
  std::vector<uint8_t> &tensor_ref() {
    return tensor_ref_buffer_;
  }

  bool good() const;

  /// Data type of contained elements
  library::NumericTypeID type() const;
  
  /// Pointer to start of device memory allocation
  void *data() const;

  /// Pointer to the first element of a batch
  void *batch_data(int batch_idx) const;

  /// Gets the layout type
  library::LayoutTypeID layout() const;

  /// Gets the stride vector
  std::vector<int64_t> const & stride() const;

  /// Gets the extent vector
  std::vector<int> const & extent() const;

  /// Gets the number of adjacent tensors in memory
  int batch_count() const;

  /// Gets the stride (in units of elements) beteween items
  int64_t batch_stride() const;

  /// Gets the stride (in units of bytes) beteween items
  int64_t batch_stride_bytes() const;

  /// Capacity of allocation in number of elements
  size_t capacity() const;
  
  /// Capacity of allocation in bytes
  size_t bytes() const;

  /// Initializes a device allocation to a random distribution using cuRAND
  void initialize_random_device(int seed, Distribution dist);

  /// Initializes a host allocation to a random distribution using std::cout
  void initialize_random_host(int seed, Distribution dist);

  /// Initializes a device allocation to a random distribution using cuRAND
  void initialize_random_sparsemeta_device(int seed, int MetaSizeInBits);

  /// Initializes a host allocation to a random distribution using std::cout
  void initialize_random_sparsemeta_host(int seed, int MetaSizeInBits);
  
  /// Uniformly fills a tensor with a value when provided o.w. zero
  void fill(double value);

  /// Copies from an equivalent-sized tensor in device memory
  void copy_from_device(void const *ptr);

  /// Copies from an equivalent-sized tensor in device memory
  void copy_from_host(void const *ptr);

  /// Copies from an equivalent-sized tensor in device memory
  void copy_to_host(void *ptr);

  /// Writes a tensor to csv 
  void write_tensor_csv(std::ostream &out);
};

using DeviceAllocationList = std::list<DeviceAllocation>;

/////////////////////////////////////////////////////////////////////////////////////////////////

} // namespace profiler
} // namespace cutlass

/////////////////////////////////////////////////////////////////////////////////////////////////